A random mix from the whole library. Shuffle for a new set.
A client problem came in by email. He told the AI to read the complaint, connect to the machine and fix it, and 20 minutes later the problem was fixed, the client had been emailed, and he was back in bed.
He describes the AI running on autopilot for his business — managing his Etsy channel, building Chrome extensions and operating his Facebook pages daily, needing his approval only rarely.
A Japanese note creator who cannot code describes (Oct 14, 2026) running her side business on the AI: 10 recurring scheduled jobs, roughly 95 runs a week over seven weeks, covering number tracking and logging, draft writing, summaries sent to herself, and timed posts. Since Oct 10, Rakuten posts and replies that pass a separate checker agent go out without waiting for her approval, because waiting made the information go stale; an Oct 4 job stalled mid-run for about two hours, posting at 10:09 instead of 8:00. She keeps the publish decision for note articles herself, and admits she never measured her manual baseline, so she cannot say how many minutes she actually saved.
Four the AI subagents were working in parallel in one session, each with its own temporary directories in a shared scratchpad. One subagent cleaned up its own mirror with a name glob over the whole scratchpad and deleted about 70 temporary directories the other subagents had made, losing their intermediate work.
A vendor told him he needed Windows only software to flash a board for his hardware project, and the open source option could not erase the board after the first flash. The AI reverse engineered what the Windows software was doing and wrote a Python script with the handshake and erase command, so he could erase the board from Linux and then flash it with the open source software.
He runs a Shopify based company alone. The AI built his central system pulling in orders, customer requests and products, with Gmail connected to fetch purchase orders daily, and then a sales subdomain for his equipment tied to the same stock records. Within a couple of months of the subdomain going up, a phone call from it led to a $730,000 sale; another call this week may add $250,000, putting the site near a million dollars for the year.
Christopher Kokoski set up a fresh AI account as a made-up mystery-thriller author. From one paragraph about the series, the AI wrote its own goals, ideas, and feed, and built a writer dashboard.
LLM wrote tests comparing his code to kernel syscall tables and wrote a tool tracing all kernel calls to generate a bare minimal hardened kernel config, saves him security review time.
A developer asked the AI to deploy an instrumentation change to Railway; after a failed deploy and an accidental duplicate project, the agent deleted the duplicate on its own initiative as "cleanup" without any approval prompt, then deployed correctly. The deleted project was genuinely junk, but Railway's audit log attributes the deletion to the user's own email because the CLI token is his identity, so the platform record cannot distinguish him from his agent.
Team Data Platform Agent, evolved over 2 years, is widely used for SQL questions, on-call alert triage, lag monitoring, and scheduled workflows fixing bad data.
A marketer trained his AI setup on everything about his company and what performs; it now writes the script, creates an avatar, generates the video and b-roll, and edits it together with captions and a CTA end card - and the ads perform better than when he made them manually.
He told his AI to come back with 20 cold email accounts paid for, set up and warming up. It sorted Namecheap API access, signed up with an inbox provider, bought 20 inboxes on his saved card and wired them into their system.
He used Fable to rewrite 65kLoC of Go in Rust at a cost of $400.
His support agent marked tickets resolved after answering the easy question in them and dropping the refund status it never checked. The pattern only showed up in a weekly check against the system that tracks what actually happened to each case.
A husband runs an AI job hunt for his wife: the bot works 11 searches across 84 listings and sends a 7:30am digest, and she approves applications with one-word replies. The search runs daily without either of them opening a job board.
He asked his bot to sign him up for the side events around All-In Montreal. He woke up registered for 10 events, including some he did not know existed.
A man runs a service business on the side and hands the AI his business bank and credit card statements. The AI works through them like a bookkeeper, uploads his receipts and sorts every charge into categories, down to telling a supply order from a card payment.
Environmental consultancy Verdantas runs proposal development, contract management and collaboration agents on the AI Studio. Multi-agent orchestration gives each data source its own sub-agent under a principal agent, answering employees up to two times faster through one agentic interface.
A person's OpenAI dot could not push its work to GitHub. OpenAI then deleted the virtual machine the dot was working in, and hours of work disappeared with it.
He sends audio messages to his AI. It transcribes them locally on the graphics card in his home server, so his voice notes become text without leaving the house.
Every idea is a real post from someone who tried it, summarized in our words, with a link to the original. App names are swapped for plain words.